The above infographic is the work of Management Consultants Oxbow Partners ^[1] and employs a novel taxonomy to categorise data teams. First up, I would of course agree with Oxbow Partners’ statement that:

Organisation of data teams is a critical component of a successful Data Strategy

Indeed I cover elements of this in two articles ^[2]. So the structure of data organisations is a subject which, in my opinion, merits some consideration.

Oxbow Partners draw distinctions between organisations where the Data Team is separate from the broader business, ones where data capabilities are entirely federated with no discernible “centre” and hybrids between the two. The imaginative names for these are respectively The Burger, The Smoothie and The Jam Doughnut. In this article, I review Oxbow Partners’s model and offer some of my own observations.

The Burger – Centralised

Having historically recommended something along the lines of The Burger, not least when an organisation’s data capabilities are initially somewhere between non-existent and very immature, my views have changed over time, much as the characteristics of the data arena have also altered. I think that The Burger still has a role, in particular, in a first phase where data capabilities need to be constructed from scratch, but it has some weaknesses. These include:

1. The pace of change in organisations has increased in recent years. Also, many organisations have separate divisions or product lines and / or separate geographic territories. Change can be happening in sometimes radically different ways in each of these as market conditions may vary considerably between Division A’s operations in Switzerland and Division B’s operations in Miami. It is hard for a wholly centralised team to react with speed in such a scenario. Even if they are aware of the shifting needs, capacity may not be available to work on multiple areas in parallel.
    
2. Again in the above scenario, it is also hard for a central team to develop deep expertise in a range of diverse businesses spread across different locations (even if within just one country). A central team member who has to understand the needs of 12 different business units will necessarily be at a disadvantage when considering any single unit compared to a colleague who focuses on that unit and nothing else.
    
3. A further challenge presented here is maintaining the relationships with colleagues in different business units that are typically a prerequisite for – for example – driving adoption of new data capabilities.

The Smoothie – Federated

So – to address these shortcomings – maybe The Smoothie is a better organisational design. Well maybe, but also maybe not. Problems with these arrangements include:

1. Probably biggest of all, it is an extremely high-cost approach. The smearing out of work on data capabilities inevitably leads to duplication of effort with – for example – the same data sourced or combined by different people in parallel. The pace of change in organisations may have increased, but I know few that are happy to bake large costs into their structures as a way to cope with this.
    
2. The same duplication referred to above creates another problem, the way that data is processed can vary (maybe substantially) between different people and different teams. This leads to the nightmare scenario where people spend all their time arguing about whose figures are right, rather than focussing on what the figures say is happening in the business ^[3]. Such arrangements can generate business risk as well. In particular, in highly regulated industries heterogeneous treatment of the same data tends to be frowned upon in external reviews.
    
3. The wholly federated approach also limits both opportunities for economies of scale and identification of areas where data capabilities can meet the needs of more than one business unit.
    
4. Finally, data resources who are fully embedded in different parts of a business may become isolated and may not benefit from the exchange of ideas that happens when other similar people are part of the immediate team.

So to summarise we have:

The Jam Doughnut – Hybrid

Which leaves us with The Jam Doughnut, in my opinion, this is a Goldilocks approach that captures as much as possible of the advantages of the other two set-ups, while mitigating their drawbacks. It is such an approach that tends to be my recommendation for most organisations nowadays. Let me spend a little more time describing its attributes.

I see the best way of implementing a Jam Doughnut approach is via a hub-and-spoke model. The hub is a central Data Team, the spokes are data-centric staff in different parts of the business (Divisions, Functions, Geographic Territories etc.).

It is important to stress that each spoke satellite is not a smaller copy of the central Data Team. Some roles will be more federated, some more centralised according to what makes sense. Let’s consider a few different roles to illustrate this:

• Data Scientist – I would see a strong central group of these, developing methodologies and tools, but also that many business units would have their own dedicated people; “spoke”-based people could also develop new tools and new approaches, which could be brought into the “hub” for wider dissemination
   
• Analytics Expert – Similar to the Data Scientists, centralised “hub” staff might work more on standards (e.g. for Data Visualisation), developing frameworks to be leveraged by others (e.g. a generic harness for dashboards that can be leveraged by “spoke” staff), or selecting tools and technologies; “spoke”-based staff would be more into the details of meeting specific business needs
   
• Data Engineer – Some “spoke” people may be hybrid Data Scientists / Data Engineers and some larger “spoke” teams may have dedicated Data Engineers, but the needle moves more towards centralisation with this role
   
• Data Architect – Probably wholly centralised, but some “spoke” staff may have an architecture string to their bow, which would of course be helpful
   
• Data Governance Analyst – Also probably wholly centralised, this is not to downplay the need for people in the “spokes” to take accountability for Data Governance and Data Quality improvement, but these are likely to be part-time roles in the “spokes”, whereas the “hub” will need full-time Data Governance people

It is also important to stress that the various spokes should also be in contact with each other, swapping successful approaches, sharing ideas and so on. Indeed, you could almost see the spokes beginning to merge together somewhat to form a continuum around the Data Team. Maybe the merged spokes could form the “dough”, with the Data Team being the “jam” something like this:

I label these types of arrangements a Data Community and this is something that I have looked to establish and foster in a few recent assignments. Broadly a Data Community is something that all data-centric staff would feel part of; they are obviously part of their own segment of the organisation, but the Data Community is also part of their corporate identity. The Data Community facilities best practice approaches, sharing of ideas, helping with specific problems and general discourse between its members. I will be revisiting the concept of a Data Community in coming weeks. For now I would say that one thing that can help it to function as envisaged is sharing common tooling. Again this is a subject that I will return to shortly.

I’ll close by thanking Oxbow Partners for some good mental stimulation – I will look forward to their next data-centric publication.
 

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Disclosure: It is peterjamesthomas.com’s policy to disclose any connections with organisations or individuals mentioned in articles. Oxbow Partners are an advisory firm for the insurance industry covering Strategy, Digital and M&A. Oxbow Partners and peterjamesthomas.com Ltd. have a commercial association and peterjamesthomas.com Ltd. was also engaged by one of Oxbow Partners’ principals, Christopher Hess, when he was at a former organisation.

 
Notes

[1]	Though the author might have had a minor role in developing some elements of it as well.
[2]	The Anatomy of a Data Function and A Simple Data Capability Framework.
[3]	See also The impact of bad information on organisations.

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